(2024) 22:326
Lin et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03497-7

BMC Medicine

RESEARCH ARTICLE

Open Access

Daytime napping and the incidenceof Parkinson’s disease: a prospective cohort
study with Mendelian randomization
Fabin Lin1,2,3,4,5†, Yisen Shi1,2,3,4†, Wenjing Song1,2,3,4†, Yanhong Weng1,2,3, Xinyang Zou1,4, Xuanjie Chen4,
Jiayi Zheng1,2,3, Ke Chen1,2,3, Qinyong Ye1,2,3*, Xilin Wu1,2,3* and Guoen Cai1,2,3*

Abstract
Background The causal relationship between daytime napping and the risk of Parkinson’s disease (PD) remains
unclear, with prospective studies providing limited evidence. This study investigated the association between daytime
napping frequency and duration and PD incidence and explored the causality relationship between this associationby conducting Mendelian randomization (MR) analysis.
Methods This prospective cohort study included 393,302 participants, and accelerometer-measured daytime
napping data were available only for 78,141 individuals. Cox proportional hazards regression was used to estimate
the association between the daytime napping frequency and duration and the PD risk. The role of the systemic
immune-inflammation index (SII) in the association between daytime napping frequency and PD risk was assessed
through mediation analyses. Moreover, the causal association between the daytime napping frequency and the PD
risk was preliminarily explored by conducting two-sample MR analyses.
Results The median follow-up duration was 12.18 years. The participants who reported napping sometimes or usually exhibited a significantly higher PD risk than those who never/rarely napped during the day [sometimes: hazard
ratio (HR), 1.13; 95% confidence interval (CI), 1.03–1.23; usually: HR, 1.33; 95% CI, 1.14–1.55], and SII played a mediating role in this association. However, the MR analyses did not indicate that the daytime napping frequency and PD
risk were significantly associated. The participants napping for over 1 h exhibited a significantly elevated PD risk (HR,1.54; 95% CI, 1.11–2.16). Moreover, no significant interaction was identified between napping frequency or durationand genetic susceptibility to PD (P for interaction > 0.05).
Conclusions In this study, increased daytime napping frequency and duration were associated with an increased
PD risk, but no causal relationship was observed between napping frequency and PD risk in the MR analysis. Larger
GWAS-based cohort studies and MR studies are warranted to explore potential causal relationships.

†

Fabin Lin, Yisen Shi, and Wenjing Song contributed equally to this work andare co-first authors.
*Correspondence:
Qinyong Yeunionqyye@163.com
Xilin Wu
xlwu@fjmu.edu.cn
Guoen Caicgessmu@126.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Lin et al. BMC Medicine(2024) 22:326

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Keywords Daytime napping, Parkinson’s disease, Mendelian randomization, UK Biobank

Background
Being the second most prevalent neurodegenerative disease
worldwide, Parkinson’s disease (PD) is the fastest-growing
neurological condition at the global scale in terms of morbidity, disability, and mortality since 1990 [1, 2]. Sleep disorder is among the most common non-motor symptomsof PD, occurring in 60–98% people diagnosed with PD.
Sleep disorder is also frequently considered among themain causes of severe discomfort in patients [3]. Daytime
napping has become a very common lifestyle habit, especially among elderly people [4]. Given the PD burden andpopularity of daytime napping, determining the association
between daytime napping and PD and developing preventive interventions are crucial public health concerns.
Few prospective studies have examined the association
between daytime napping and PD risk, and their findings
have been inconsistent. The Honolulu-Asia Aging Study,
which included 3078 older adult men, reported no association between daytime napping and PD risk [5]. However,some other studies have drawn the opposite conclusion.
For example, in a study based on osteoporotic fracturesin men, objective prolonged napping was associated withan increased PD risk in older men [6]. Moreover, a crosssectional study suggested that daytime napping is significantly associated with PD in older adult women [7]. In
another study analyzing an older US population, longer
daytime naps were associated with higher odds of PD [8].
However, considering the presence of study design limitations or population generalizability issues, further studies are warranted to assess this association.
By using self-reported and accelerometer-measured
napping data from a large-scale population-based UK
Biobank study, this study explored the relationshipbetween daytime napping frequency and PD incidence
through prospective observational analysis and Mendelian randomization (MR). MR analysis leverages genetic
variants that affect modifiable risk factors to infer causal
relationships between these exposures and health outcomes. This method is generally regarded as more robust
against confounding factors and reverse causation compared to traditional observational approaches [9]. We
also investigated, based on the polygenic risk score (PRS),
whether daytime napping was associated with disease
onset in a PD-susceptible population.
Methods
Study population

Between 2006 and 2010, the UK Biobank recruited
approximately 500,000 UK residents who were aged
37–73 years at the time of recruitment. These participants

provided regular blood, urine, and saliva samples anddetails about their lifestyles for analysis. These data
were then linked to their health-related records. The UK
Biobank conducted three major resurveys. The first resurvey was conducted in 2012–2013 and involved approximately 20,000 participants, the second resurvey started
in 2014 and involved approximately 65,000 participants, and the third resurvey began in 2019 and involved
approximately 5000 participants. This prospective cohort
study was approved by the UK North West Multi-Centre Research Ethics Committee. Written informed consent was obtained from all participants [10]. The study
was conducted in accordance with the Strengthening
the Reporting of Observational Studies in Epidemiology(STROBE) reporting guidelines. Fig. S1 in the Additional
File 2 presents the detailed participant inclusion andexclusion process in this study.
Assessment of exposure

The daytime napping frequency was determined basedon the participant’s response to the question “Do you
have a nap during the day?” presented on a touch
screen questionnaire. The response options included
were never/rarely, sometimes, usually, and prefer not
to answer. The option “Prefer not to answer” was categorized as missing data. The stability of the napping
exposure frequency was verified using the CochranMantel–Haenszel test [11]. The daytime napping duration was determined using the wrist-worn accelerometerdata (Field 40030), which provided average sleep rates
during each period. The sleep state was calculated using
a combination of data sources and methodologies [12].
To deduce sleep state, the participants were made towear a wrist-worn triaxial accelerometer (Axivity AX3)
for capturing tri-axial acceleration data at 100 Hz [13–15]. On the study measurement day, a Vicon autographer
wearable camera was worn by the participants during
wakeful periods. The camera took photos every approximately 20 s [16]. Annotated images from the wearable
camera were employed to construct a groundtruth ofreference behaviors, including sleep. Sleep information
was obtained from a simple sleep diary and a HETUS
time-use diary. The groundtruth resulted in 213 activity labels, which were condensed into six free-living
behavior labels, including “sleep.” Accelerometer data
were preprocessed using standard procedures, including
device calibration, resampling to 100 Hz, and removal ofnoise and gravity [17]. Features were extracted from 30-stime windows, which resulted in a 126-dimensional feature vector. Random forests (RFs) were used to classify

Lin et al. BMC Medicine(2024) 22:326

the activities, thereby providing predictions based on an
aggregate of individual CART trees. Balanced RFs were
employed to account for the unbalanced dataset [18].
RFs classify data points but have a dearth of understanding of the temporal structure. A hidden Markov model(HMM) was used for time smoothing by encoding the
temporal sequence of classes [19]. The Viterbi algorithm
was applied to identify the most likely sequence of true
activity states given observed emissions, thus correcting
erroneous predictions. The HMM, which incorporated
the temporal structure, was used to infer the most likely
sequence of true activity states. The state of sleep, a specific behavior label, was deduced using the correctedsequence of predicted classes. The model trained on
free-living groundtruth data was used to predict behavior in a large health dataset (UK Biobank) with 103,712
participants. The probability of engaging in specificbehavior types, including sleep, was expressed for each30-s epoch. In summary, the sleep state was deducedby collecting information from wearable cameras, sleep
diaries, and accelerometer data. Using machine learning,
including RFs and HMMs, helped to improve the accuracy of sleep state prediction and correct errors in initial classifications. We used 9 a.m. and 6 p.m. as the startand end times for daytime napping because our study
included elderly participants and who are known to havephase advance (earlier bedtime) [20]. Additionally, we
avoided potential morning and evening transition periods (7 a.m. to 9 a.m. and 6 p.m. to 8 p.m.). To eliminate
interference from participants’ main sleep periods during the day, the habitual sleep/wake times were calculated for the individuals. Specifically, sleep scores were
presented for each period based on the weighted sum of
the current period, four preceding periods, and two subsequent periods. The calculation involves determining
whether the sum is smaller than 1 h (indicating sleep) or
not (indicating wakefulness). This methodology has been
described in studies related to the UK Biobank database[21].
Systemic immune‑inflammation index

Peripheral blood samples collected from the UK Biobank
participants were analyzed by the UK Biobank Central
Laboratory within 24 h of sampling. The Beckman Coulter LH750 Hematology Analyzer was used to examine theblood cell samples in 4 mL of ethylenediaminetetraacetic
acid vacuole (details at https://​bioba​nk.​ndph.​ox.​ac.​uk/​
ukb/​ukb/​docs/​haema​tology.​pdf.). The systemic immuneinflammation index (SII) was calculated using the following formula: SII = (neutrophil count * platelet count)/
lymphocyte count [22].

Page 3 of 12

Assessment of outcomes

As recommended by the UK Biobank and previous studies [23, 24], algorithmically defined outcomes were used
for determining PD onset in the cohort participants. Disease-related information was gathered by reviewing inpatient electronic health records and death registers linked
to the Hospital Episode Statistics England, Scottish Morbidity Records, and Patient Episode Database for Waleson October 31, 2022 (England), July 31, 2021 (Scotland),and February 28, 2018 (Wales), respectively. Follow-up
lasted from baseline to the time of PD diagnosis, death,loss to follow-up, or review, whichever occurred first.
Polygenic risk score

The polygenic risk score (PRS) represents the correlation
between the genotype and the PD risk. We here used thestandard PRS for PD released by the UK Biobank (Field
ID: 26,260), which was extracted from an external GWAS
meta-analysis dataset, as described by Thompson et al.
[25]; these authors had calculated PRS for 28 diseases
and 25 quantitative traits. By using a Bayesian approach,the RPS algorithm was built based on trait-specificmeta-analyses. An individual’s PRS was calculated as the
genome-wide sum of posterior effect sizes per variant
multiplied by the allelic dose. The raw PRS was centeredand normalized to produce a corrected PRS for subsequent analyses.
Two‑sample MR

For determining the daytime napping frequency, 104
independent single nucleotide polymorphisms (SNPs)(P < 5 × ­10−8, r2 < 0.001, distance = 10,000 kb) from the
GWAS study at the UK Biobank (n = 452,633) wereused. These SNPs were replicated and validated in the
23andMe cohort (n = 541,333) [26]. The daytime napping
category (never, sometimes, or usually) was considereda continuous variable in the MR analysis. Genotyping,quality control, and interpolation procedures for the UK
Biobank data have been described elsewhere [27].
The GWAS summary data for PD were obtained fromGWAS Catalog (https://​www.​ebi.​ac.​uk/​gwas/​home) andincluded 42,792 PD patients and 568,693 controls (Study
ID: GCST90275127) [28].
Valid instrumental variables (IVs) had to satisfy the following three assumptions: (1) association with the risk
exposure of interest (relevance), (2) no common causewith the outcome (independence), (3) affect the outcome
only through the risk exposure (exclusion restriction). To
ensure the reliability of the findings, the PLINK clumping
method with a stringent clumping threshold (r2 < 0.001,
LD distance = 10,000 kb) was applied. This ensured that

Lin et al. BMC Medicine(2024) 22:326

SNPs in the residual linkage-disequilibrium (LD) withina particular window were pruned to evaluate the bias
caused by the residual LD of genetic variants. For harmonization, the strand for non-palindromic SNPs wascorrected. For palindromic SNPs, rather than excluding
them from the analysis, an attempt was made to infer
the alleles on the positive strand using effect allele frequencies. Additionally, we estimated the F-statistic for
instrumental variables, and the mean of F > 10 is generally sufficient for the MR analysis [29, 30]. The Mendelianrandomization study followed the STROBE-MR guidelines [31, 32]. The STROBE-MR checklist can be found in
Additional File 1.
Statistical analyses

Hazard ratios (HRs) and 95% confidence intervals (CIs)for the UK Biobank-based observational analysis were
estimated using the Cox proportional hazards model.
The group of participants who never napped was used asa reference for this estimation. We reported the results
based on three models: (1) unadjusted; (2) adjusted forage and sex; (3) adjusted for age, sex, ethnicity, bodymass index (BMI), household income, education, smoking status, alcohol intake frequency, total hours of physical activity per week, family history of PD, and othersleep-related covariates (Additional File 2: Text. S1). The
association between daytime napping and the PD risk invarious genetic risk groups was determined using a cutoff point stratified by PRS tertiles for PD. The interaction between PRS and daytime napping in their effect on
PD development was investigated using likelihood ratio
tests. A series of sensitivity analyses were conducted:(1) excluding PD events in the previous 2 or 4 years; (2)excluding self-reported PD outcomes; (3) excluding participants who reported working at night; (4) excluding
participants who reported having sleep disorders; (5)including death as a competing risk through a competing risk model; (6) further correcting for difficulty inwaking up at morning, chronotype, sleeplessness, snoring, and daytime dozing as sleep-related conditions.
Using a regression-based approach, mediation analyses
were performed to decompose the direct, indirect, andtotal effects between daytime napping and PD and to
estimate the proportion of this SII-mediated association. In the mediation analyses, the effect of the exposure variable on the mediator variable was defined as“a,” the effect of the mediator variable on the outcome
was defined as “b,” and the total effect of the exposure
variable on the outcome was defined as “c.” According
to previous studies [33], the effect of the exposure variable on the outcome through the mediating variable was“a*b.” Thus, the proportion of mediation was (a*b)/c.
The number of boots applied for the mediation analyses

Page 4 of 12

was 200. The mediation analysis was conducted after
adjustment for the confounding factors of age, gender,
smoking status, alcohol consumption, BMI, and physical activity.
To completely examine the causal association between
daytime naps and PD, a bidirectional two-sample MR was
conducted. Two-sample MR methods, wherein exposure
and outcome are measured in non-overlapping datasets,allow for minimal false-positive rates and increased sample sizes [34]. In the MR analysis, random-effects inverse
variance weighting (IVW) was used as the primarymethod, and MR-Egger regression [35], weighted medianmodels [36], simple mode, and weighted mode were usedas secondary methods [37]. The Wald ratio test was used
when < 3 SNPs were available for the analysis. To prevent
any potential violations of MR assumptions, we tested forpleiotropy (through MR-Egger regression intercepts, and
MR Pleiotropy Residual Sum and Outlier [MR-PRESSO]
global test) and heterogeneity (based on Cochran’s Q values [38]).
In two-sample MR, we first performed preliminary
analyses based on IVW and four other secondary analyses. In the sensitivity analysis, to avoid reverse causation, the Steiger test was conducted for each SNP so as
to determine whether the exposure R2 (the variance ofthe disease/trait explained by the selected SNP) was
greater than the outcome R2, excluding SNPs that tested“False” (outcome R2 > exposure R2) [39]. When pleiotropy or heterogeneity was identified (P < 0.05), then weapplied MRPRESSO to identify and eliminate outliers until no more outliers were identified [40]. We thenperformed Radial-MR to determine whether outliers
were present (the threshold was set at 0.05) [41], and, if
outliers were present, they were eliminated and RadialMR was repeated until no outliers were detected. Afterexcluding the abovementioned SNPs, the analyses werereconducted.
Statistical analysis was conducted in R, Version 4.2.3.
The MR analysis was conducted using the “TwoSampleMR” (Version 0.5.6) and “RadialMR” software
package.

Results
Descriptive characteristics of the prospective cohortThis prospective cohort study involved 393,302 participants [mean age: 56.27 years; 186,812 males (47.5%)] anda median follow-up period of 12.18 (SD = 1.83) years. Of
them, 224,646 (57.1%) participants reported they never/rarely took a daytime nap, 148,094 (37.7%) participants
napped sometimes, and 20,562 (5.2%) participants usually
napped during the day. Compared with those reportingto never/rarely take a daytime nap, the participants who
napped sometimes or usually were more likely to be older,

Lin et al. BMC Medicine(2024) 22:326

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Table 1 Baseline characteristics of participants for nap frequency
Characteristics

Daytime napping
OverallP-value
Never/rarely

Sometimes

Usually

Participants, n

39330222464614809420562

Age, Mean(SD)56.27 (8.11)

55.20 (8.11)

57.47 (7.92)

59.38 (7.53)<0.001206490 (52.5)

128615 (57.3)

71261 (48.1)

6614 (32.2)<0.001

Sex, n (%)

FemaleMale

186812 (47.5)

96031 (42.7)

76833 (51.9)

13948 (67.8)

Ethnicity, n (%)

White

360803 (91.7)

206781 (92.0)

135275 (91.3)

18747 (91.2)

Black2413 (0.6)

948 (0.4)1294 (0.9)

171 (0.8)

Asia6632 (1.7)

3345 (1.5)

2794 (1.9)

493 (2.4)

Other23454 (6.0)

13572 (6.0)

8731 (5.9)

1151 (5.6)

27.30 (4.70)

26.83 (4.48)

27.85 (4.86)

28.52 (5.23)<0.001

Greater than 100,000 £

20800 (5.3)

14535 (6.5)

5804 (3.9)

461 (2.2)<0.001

BMI (kg/m2), Mean(SD))
Household income, n (%)

Education, n (%)

Current tobacco smoking, n (%)

Alcohol, n (%)<0.001

18,000 £ to 100,000 £256381 (65.2)

152000 (67.7)

92582 (62.5)

11799 (57.4)

Less than 18,000 £

73558 (18.7)

34639 (15.4)

32820 (22.2)

6099 (29.7)

Unknow

42563 (10.8)

23472 (10.4)

16888 (11.4)

2203 (10.7)

Any school degree46249 (11.8)

27809 (12.4)

16365 (11.1)

2075 (10.1)

College or University degree 138859 (35.3)

84795 (37.7)

47784 (32.3)

6280 (30.5)

Vocational

20452 (5.2)

11763 (5.2)

7799 (5.3)

890 (4.3)

Other

187742 (47.7)

100279 (44.6)

76146 (51.4)

11317 (55.0)

No353469 (89.9)

204166 (90.9)

131604 (88.9)

17699 (86.1)

Only occasionally10873 (2.8)

6068 (2.7)

4200 (2.8)

605 (2.9)

Yes, on most or all days

28960 (7.4)

14412 (6.4)

12290 (8.3)

2258 (11.0)

Never15388 (3.9)

8101 (3.6)

6223 (4.2)

1064 (5.2)

Previous

13316 (3.4)

6350 (2.8)

5739 (3.9)

1227 (6.0)

364598 (92.7)

210195 (93.6)

136132 (91.9)

18271 (88.9)

Current<0.001<0.001<0.001

Physical activity (minutes/week),Mean(SD)

2651.30 (2708.29) 2647.33 (2668.25) 2654.96 (2733.00) 2668.35 (2952.70) 0.457

Sleep duration (hours/day),Mean(SD)

7.16 (1.08)

Family history of PD, n (%)

7.07 (0.99)

7.23 (1.12)

7.69 (1.54)<0.0010.103

No377120 (95.9)215524 (95.9)

141917 (95.8)

19679 (95.7)

Yes16182 (4.1)

9122 (4.1)

6177 (4.2)

883 (4.3)

Abbreviation: BMI body mass index, PD Parkinson’s disease, SD standard deviation
Data are presented as mean (standard deviation) or n (%). The one-way ANOVA tests were used for continuous variables and χ2 tests were used for categoricalvariables

male, poorer, and current smokers; have a higher BMI; be
non-drinkers; and have received lower levels of education(Table 1). Furthermore, the participants who found waking up in the morning difficult, who found being a “morning” person difficult, who had insomnia, who snored, andwho had the habit of daytime dozing had a higher daytime napping frequency (Additional File 2: Table S1). The
study investigated the daytime napping duration of 83,151
participants. Table S2 in Additional File 2 presents theirbaseline characteristics. The baseline characteristics of the
16,535, 54,769, and 4581 participants in the first, second,
and third follow-up visits, respectively, are presented inTables S3–S5 in Additional File 2.

Association between daytime napping and PD risk

After a range of covariates were adjusted, a significantly
increased PD risk was noted among the participants
who reported taking a daytime nap sometimes or usually compared with among those who never/rarely tooka daytime nap. The participants who reported napping
sometimes had a 13% higher PD risk (HR, 1.13; 95%
CI, 1.03–1.23; P = 0.009) compared with those reported
never. The participants who reported frequent daytime napping had a 33% increased PD risk (HR, 1.33;
95% CI, 1.14–1.55; P = 0.001) compared with thosereported never (Table 2). Similar results were observed
when the three follow-up visits were assessed separately

Lin et al. BMC Medicine(2024) 22:326

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Table 2 Association between daytime napping frequency and incidence of Parkinson’s disease (N=393302)
Characteristic

Daytime napping
Never/rarely

Sometimes

Usually

Participants, n (%)

224646 (57.1%)

148094 (37.7%)20562 (5.2%)

person-years

2753818

1792200

243552

Cases

972

1010

223

Incident cases per 100 000 person-years 35.3

56.36

91.56

ModelsModel 11.00 (Reference)HR (95%CI) P-value

HR (95%CI) P-value

1.61 (1.47-1.75) <0.001

2.63 (2.28-3.04) <0.001

Model 21.00 (Reference)

1.16 (1.06-1.26) 0.001

1.40 (1.21-1.63) <0.001

Model 31.00 (Reference)

1.13 (1.03-1.23) 0.009

1.33 (1.14-1.55) <0.001

Abbreviation: HR hazard ratio, BMI body mass index
Model 1: unadjusted
Model 2: age, sex
Model 3: age, sex, race, household income, education, BMI, current tobacco smoking, physical activity, alcohol, Sleep duration, and family history of Parkinson’s
diseaseTable 3 Association between daytime napping during and incidence of Parkinson’s disease (N=78141)
Characteristic

Daytime napping
0 hour

≥1 hour<1 hourParticipants, n (%)

20837 (26.7%)

43914 (56.2%)

13390 (17.1%)

person-years

258844560511165227

Cases

70

131

75

Incident cases per 100 000
person-years

27

23

45

ModelsContinues

HR (95%CI) P-value

HR (95%CI) P-value

HR (95%CI) P-value

Model 11.00 (Reference)

0.89 (0.67-1.19) 0.430

1.69 (1.22-2.34) <0.001

1.01 (1.00-1.01) <0.001

Model 21.00 (Reference)

0.87(0.65-1.17) 0.350

1.48 (1.07-2.05) 0.020

1.01 (1.00-1.01) <0.001

Model 31.00 (Reference)

0.89 (0.66-1.19) 0.431

1.54 (1.11-2.16) 0.01

1.01 (1.00-1.01) <0.001

Abbreviation: HR hazard ratio, BMI body mass index
Model 1: unadjusted
Model 2: age, sex
Model 3: age, sex, race, household income, education, BMI, current tobacco smoking, physical activity, alcohol, sleep duration, and family history of Parkinson’s disease(Additional File 2: Table S6). The participants who
napped less than an hour a day exhibited an increased
PD risk, whereas those who napped over an hour a day
displayed an 54% increased PD risk (HR, 1.54; 95% CI,
1.11–2.16; P = 0.01; Table 3). Afternoon naps were associated with an increased PD risk [13:00 pm–15:00 pm:
1.02 (1.01–1.03), P < 0.001, 16:00 pm–18:00 pm: 1.01
(1.00–1.02), P = 0.001], whereas morning naps are notassociated with the PD risk (9:00 am–12:00 am: 1.00
(1.00–1.01), P = 0.107) (Additional File 2: Table S7). The
dose–response analysis revealed a positive linear relationship between the daytime napping duration and the
PD risk (Fig. 1). The results remained robust throughthe sensitivity analysis (Additional File 2: Table S8-S13).

Joint association between the PRS, daytime napping,and PDNo significant interaction was observed between thedaytime napping duration and PRS in their effect on PD
development (P for interaction = 0.178; Table 4). Similarly, no interaction was observed between the daytime
napping frequency and PRS in their effect on PD occurrence (P for interaction = 0.225).
MR analysis

Table S14 in the Additional File 2 presents the twosample MR results before excluding the outliers identified by MRPRESSO or Radial-MR. The F-statistics forindividual SNPs were presented in Table S15 in Additional File 2. Regarding the impact of daytime nappingLin et al. BMC Medicine(2024) 22:326

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Fig. 1 Multivariable adjusted dose–response associations between daytime napping duration and risk of incident Parkinson’s disease. Adjusted
for age, sex, race, household income, education, BMI, current tobacco smoking, physical activity, alcohol, sleep duration, and family history
of Parkinson’s disease

Table 4 Risk of incident Parkinson’s disease according to daytime napping status stratified by P
­ RSa (N=384242 and 78141)
Daytime napping

Frequency (N=384242)

Duration (N= 78141)

PRS
Low

IntermediateHighHR (95%CI) P-value

HR (95%CI) P-value

HR (95%CI) P-value

Never/rarely

1.00 (Reference)

1.00 (Reference)

1.00 (Reference)

Sometimes

1.2 (0.99-1.46, p=0.065)

1.05 (0.89-1.23, p=0.594)

1.15(1.01-1.31, p=0.041)

Usually

1.28 (0.92-1.77, p=0.143)

1.51(1.17-1.96, p=0.002)

1.2 (0.95-1.52, p=0.125)

0 hour

1.00 (Reference)

1.00 (Reference)

1.00 (Reference)

1 hour

1.17 (0.61-2.23) 0.63

0.73(0.42-1.26) 0.25

0.89 (0.59-1.34) 0.59

≥1 hour

1.96 (0.96-4.03) 0.07

1.00 (0.51-1.96) 0.99

1.76(1.11-2.80) 0.02

P value for­interactionb

0.225

0.178

Abbreviation: HR hazard ratio, BMI body mass indexaModel adjusted for age, sex, race, household income, education, BMI, current tobacco smoking, physical activity, alcohol, sleep duration, and family history ofParkinson’s diseaseb

P value for the interaction between daytime napping and polygenic risk scoreon the incidence of PD, the random-effects IVW model
showed no significant association between them (OR,0.816, 95% CI, 0.510 to 1.304). This result remained
consistent in other models. No heterogeneity or horizontal pleiotropy was detected (P > 0.05). With PD as
exposure, the random-effects IVW model displayedno significant association between genetic liability
to PD and daytime napping (β, 0.008, 95% CI, − 0.006

to 0.022). However, IVW and MR-Egger revealed
the presence of heterogeneity and the MRPRESSO
global test displayed the presence of horizontal pleiotropy (P < 0.05). In the sensitivity analysis, SNPs that
did not pass the Steiger test, and outliers identified
by MRPRESSO or Radial-MR have been displayed inTable S16 in Additional File 2. After the outliers were
removed, no significant heterogeneity or horizontal

Lin et al. BMC Medicine(2024) 22:326

pleiotropy was observed (Additional File 2: Table S17).
The random-effects IVW indicated that no significant
association between genetic liability to PD and daytime napping (β, − 0.003; 95% CI, − 0.008 to 0.002). This
result was consistent across other models. Moreover,the leave-one-out method was employed to test the stability of the results (Additional File 3: Fig. S2 and Additional File 4: Fig. S3).
Mediating role of SII in the association
between the daytime napping frequency and PD incidence

Fig. S4 in Additional File 2 presents the results of mediation analyses for the association between SII and PD
occurrence. Each unit increase in SII (Ln-transformed)
was associated with a 19.1% increase in the PD risk (HR,1.191; 95% CI, 1.098–1.291). For the association between
the daytime napping frequency and SII, the participants
who reported napping sometimes (β, 0.015; 95% CI,
0.012–0.019) or usually (β, 0.033; 95% CI, 0.025–0.040)
had significantly higher SII levels compared with those
who reported never napping. The mediation analyses
revealed that SII mediated 1.75% (95% CI, 0.83–4.12%)
and 2.05% (95% CI, 1.02–4.81%) of the association in theeffect of sometimes and usually napping, respectively, on
PD development.

Discussion
This large prospective cohort study found that the higher
frequency and longer duration of daytime napping wereassociated with an increased PD risk. The MR analysis
revealed no reciprocal causal relationship between thedaytime napping frequency and the PD risk. Additionally,
the higher frequency of daytime napping was associatedwith an increase in the immune inflammatory response,
which elevated the PD risk. Moreover, no significant
interaction was observed between the frequency andduration of daytime napping and PRS in their effect on
the PD risk.
The observational study results align with those ofnumerous epidemiologic studies, indicating that theincreased daytime napping frequency and longer nap
duration are linked to a higher PD risk. In a multicenterprospective study, 2920 men without a history of PD
were followed for 11 years. The study ultimately revealed
106 PD events and demonstrated a correlation between
objectively prolonged napping and a higher PD risk inolder men [6]. A multicenter cohort study of 2675 community-dwelling older women reported that both subjective and objective naps were associated with PD [7]. Ina United States-based cohort study with 213,885 volunteers having no history of PD, subjective daytime napping was associated with a higher PD risk [8]. A previous

Page 8 of 12

study based on the Honolulu-Asia Aging Study revealed
that subjective daytime napping is associated with the PD
risk in men [5].
The MR analysis revealed no causal relationshipbetween the daytime napping frequency and the PD risk.
Nevertheless, caution is warranted in interpreting thesefindings because of the small sample size and low precision of our study. The analyses were also constrainedby a rudimentary assessment of the daytime napping
frequency through a questionnaire, which resulted
in the lack of details on duration or timing [26]. Our
attempts to partially validate the specificity of identified
loci, transitioning from self-reporting of data to objective determination through accelerometers, may have
faced limitations because of the phenotypic differences
between self-report and accelerometer data. Additionally, the relatively small sample size in the accelerometersubsample and the time lag between measurements, withaccelerometers worn between 2 and 10 years after the
study baseline constrained the assessment. Moreover,additional larger studies using GWAS-based cohorts andMR approaches are necessary for thoroughly investigating the relationship between the daytime napping frequency and the PD risk.
A definitive physiological mechanism directly explaining the association between daytime napping and anelevated PD risk is lacking and remains a challenge. In
a study, sleep was objectively measured in older adults
from the community, not clinically diagnosed with PD,
by using an activity recorder [42]. The study revealed thatthe degree of sleep fragmentation was associated with
Lewy body pathological burden as well as nigrostriatal
neuronal loss. The increased odds of developing PD werecorrelated with greater sleep fragmentation, which, inturn, was associated with Lewy body pathology burden
and nigrostriatal neuron loss [43]. Sleep fragmentation
can possibly contribute to PD pathology, with potential mechanisms involving oxidative stress promotion or
impairment in toxic protein clearance. In model organisms, both sleep fragmentation and sleep deprivation areassociated with brain oxidative stress, a factor linked to
PD pathogenesis [44–46]. Moreover, napping might be
an integral component of the PD pathological process.
The suprachiasmatic nucleus (SCN), which plays a crucial role in regulating the sleep–wake cycle, may contribute to reduced melatonin secretion and sleep–wake
disruption, particularly in elderly healthy individuals withdecreased SCN activity [47, 48]. Animal model studies
have revealed abnormal electrical activity in the SCN ofα-synuclein-overexpressing mouse models of PD [49].
Subsequent research should delve deeper into the underlying mechanisms.

Lin et al. BMC Medicine(2024) 22:326

The examination of the interaction between the daytime napping frequency or duration and the PRS on the
PD risk yielded no significant results. This lack of significance suggests that genetic factors does not influence
the association between daytime napping and the PD
risk. These findings align with those of prior research.
Benjamin et al. analyzed the interaction between PRS
and a precursor symptom of PD, “sleepiness” [50]. The
results of this analysis unveiled no significant interaction, further confirming that genetic factors are notsubstantially involved in the relationship between daytime napping and the PD risk.
Higher daytime napping frequency and longer
daytime napping duration increased the PD risk byincreasing the immune inflammatory response. Considering that daytime napping may be associated withan increase in the levels of inflammatory markers, theeffect of napping on PD was hypothesized to be related
to the inflammatory response [51]. A series of crosssectional studies were conducted in a community-based
population in China. Longer daytime naps were correlated to higher IL-6 levels. Inflammatory cytokines may
be served as a crucial link between daytime napping
and nonalcoholic fatty liver disease. While inflammation and immune dysfunction impair neuronal healthand survival and are associated with PD, including boththe motor and non-motor components of PD [52]. A
meta-analysis included 152 studies and extracted studies reporting concentrations of blood or cerebrospinal
fluid (CSF) markers in PD patients and controls. Significant alterations in the levels of inflammatory markers were observed between the PD group and controls.
Increased IL-6, TNF-α, IL-1β, STNFR1, CRP, CCL2,
CX3CL1, and CXCL12 levels and decreased INF-γ and
IL-4 levels were observed in the PD group. In addition,
increased CSF levels of IL-6, TNF-α, IL-1β, CRP, andCCL2 were observed in the PD group compared withthe control group [53].
The direction of the relationship is a key issue in the
study of sleep and PD. Thus, benefiting from the long
follow-up of the UK Biobank study, we found similar
results when PD cases were excluded at risk 2 years and4 years after the baseline, a method reported in previous
studies [6].

Limitations
This study has some limitations. First, although we
adjusted for most known PD-associated confounding
variables, some unknown confounders may have exerted
their effects. Second, the latency period for the rapid eye
movement sleep behavior disorder as a pre-Parkinsonian
symptom can be up to 10 years. Although our sensitivity

Page 9 of 12

analysis excluded patients with sleep disorders, bias may
have been introduced by the occult population. Third,a fundamental assumption of MR is that SNPs are notlinked to any confounders of the exposure or the outcome. Even when pleiotropic bias is taken into account,no MR study can entirely eliminate the possibility of pleiotropic bias. Finally, the UK Biobank participants werepredominantly Europeans. Further studies are warranted
to investigate the extent to which these findings can beapplied to other populations.

Conclusions
The results of this large UK Biobank-based cohort study
suggested that the increased frequency of self-reported
daytime naps and duration of accelerometer measurements are associated with an increased PD risk. The
MR analysis provided no evidence that the frequency of
daytime naps was associated with PD outcomes. Further
larger GWAS-based cohort and MR studies are warranted to validate the findings.
Abbreviations
BMI	Body mass index
CIs	Confidence intervals
CSF	Cerebrospinal fluid
GWAS	Genome-wide association study
HMM	Hidden Markov modelHRs	Hazard ratios
IVW	Inverse variance weightingLD	Linkage disequilibrium
MR	Mendelian randomization
MRPRESSO	MR pleiotropy residual sum and outlier
PD	Parkinson’s disease
PRS	Polygenic risk scoreRFs	Random forests
SCN	Suprachiasmatic nucleus
SII	Systemic immune-inflammation index
SNPs	Single nucleotide polymorphisms
STROBE	
Strengthening the Reporting of Observational Studies in
Epidemiology

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03497-7.
Additional file 1. STROBE-MR checklist.
Additional file 2: Text S1, Table S1-S15, and Figures S1, S3. Text S1- [Assessment of covariates]. Table S1- [Baseline sleep characteristics of participants
based on frequency of daytime naps]. Table S2- [Baseline characteristics
of participants based on daytime napping during]. Table S3- [Baselinecharacteristics of participants based on daytime napping frequency(First repeat assessment visit (2012–2013))]. Table S4- [Baseline characteristics of participants based on daytime napping frequency (Imaging
visit (2014 +))]. Table S5- [Baseline characteristics of participants basedon daytime napping frequency (First repeat imaging visit (2019 +))].
Table S6- [Daytime nap frequency and Parkinson’s disease prevalence
when using different time points as a baseline]. Table S7- [Relationshipbetween daytime napping duration and onset of Parkinson’s disease
when napping was taking place at different times (N = 78,141)]. Table S8[Association between daytime napping and incident Parkinson’s disease
after excluding participants who experienced an outcome event within
the first two years of follow-up]. Table S9- [Association between daytime

Lin et al. BMC Medicine(2024) 22:326

napping and incident Parkinson’s disease after excluding participants who
experienced an outcome event within the first four years of follow-up].
Table S10- [Association between daytime napping and incident Parkinson’s disease after excluding participants who self-reported Parkinson’s
disease]. Table S11- [Association between daytime napping and incident
Parkinson’s disease after excluding participants who worked night shifts].
Table S12- [Association between daytime napping and incident Parkinson’s disease after excluding participants with sleep disorders]. Table S13[Associations between daytime napping and incident Parkinson’s disease
by treating all-cause death as a competing risk]. Table S14- [Analysis of
the association between daytime napping and PD with bidirectional
two-sample Mendelian randomization]. Table S15- [Outliers identified in
MR-PRESSO and Radial-MR]. Fig S1- [Flow chart of the screening process
for this study]. Fig S4- [Mediation analysis of the role of systemic immuneinflammation index (SII) in the association between nap frequency andthe development of PD].
Additional file 3: Fig S2- [(A) Scatter plots of genetic associations withdaytime napping against the incidence of PD. (B) Funnel plot to assessheterogeneity. The blue line represents the inverse-variance weighted
estimate, and the dark blue line represents the Mendelian randomizationEgger estimate. (C) Forest plot of the causal effects of single nucleotide
polymorphisms associated with daytime napping on PD. The red lines
are MR results of MR-Egger test and IVW method. (D) MR leave-one-out
sensitivity analysis for daytime napping frequency on PD. Each black point
represents the IVW-MR method applied to estimate the causal effect ofdaytime napping frequency on PD excluding that particular variant fromthe analysis].
Additional file 4: Fig S3- [(A) Scatter plots of genetic associations withgenetic liability to PD on daytime napping. (B) Funnel plot to assessheterogeneity. The blue line represents the inverse-variance weighted
estimate, and the dark blue line represents the Mendelian randomizationEgger estimate. (C) Forest plot of the causal effects of single nucleotide
polymorphisms associated with genetic liability to PD on daytime napping. The red lines are MR results of MR-Egger test and IVW method. (D)MR leave-one-out sensitivity analysis for genetic liability to PD on daytime
napping. Each black point represents the IVW-MR method applied toestimate the causal effect of genetic liability to PD on daytime napping
excluding that particular variant from the analysis].
Acknowledgements
This research was conducted under application number 94166 from the
UK Biobank resource. We would like to express our sincere thanks to all the
researchers involved in the UK Biobank.
Authors’ contributions
F.B.L., Y.S.S., W.J.S., Q.Y.Y., X.L.W., and G.E.C contributed to the concept anddesign of the study. F.B.L. and Y.S.S. were involved in the acquisition of data,
data generation, and data cleaning. F.B.L. and W.J.S. did the analysis and interpretation of data. F.B.L., Y.H.W., X.Y.Z., K.C., X.J.C., J.Y.Z., and Q.Y.Y. contributed
to the drafting of the article and revising it critically. Q.Y.Y., X.L.W. and G.E.C
provided academic guidance for this study. All authors read and approved thefinal manuscript.
Funding
This work was funded by grants from the Fujian Provincial Financial Special
Project (2023CZ002), Excellent Young Scholars Cultivation Project of Fujian
Medical University Union Hospital (2022XH030), National Key Clinical Specialty(21281003), the Fujian Province Key Clinical Specialty (2128100537), andClinical Research Center for Precision Diagnosis and Treatment of Neurological Diseases of Fujian Province (2022Y2005). The funders played no role in the
study design or implementation; data collection, management, analysis orinterpretation; manuscript preparation, review or approval; or the decision tosubmit the manuscript for publication.
Availability of data and materials
All data used for Mendelian randomization analyses are publicly available fromthe respective GWAS. Cohort data from UK Biobank are available after theapplication (https://​www.​ukbio​bank.​ac.​uk/).

Page 10 of 12

Declarations
Ethics approval and consent to participate
All participants gave written informed consent prior to data collection. UK
Biobank has full ethical approval from the NHS National Research Ethics
Service (16/NW/0274).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road,
Fuzhou 350001, China. 2 Fujian Institute of Geriatrics, Fujian Medical University
Union Hospital, 29 Xinquan Road, Fuzhou 350001, China. 3 Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road,
Fuzhou 350001, China. 4 Fujian Medical University, Fuzhou 350001, China.
5Department of Neurosurgery, Fujian Medical University Union Hospital, 29
Xinquan Road, Fuzhou 350001, China.
Received: 12 December 2023 Accepted: 21 June 2024

References
1. Dorsey ER, Constantinescu R, Thompson JP, Biglan KM, Holloway RG,
Kieburtz K, Marshall FJ, Ravina BM, Schifitto G, Siderowf A, et al. Projected
number of people with Parkinson disease in the most populous nations,2005 through 2030. Neurology. 2007;68(5):384–6. https://​doi.​org/​10.​
1212/​01.​wnl.​00002​47740.​47667.​03.
2. Global, regional, and national burden of Parkinson’s disease, 1990–2016:a systematic analysis for the Global Burden of Disease Study 2016. Lancet
Neurol. 2018;17(11):939–953. https://​doi.​org/​10.​1016/​s1474-​4422(18)​30295-3.
3. Rothman SM, Mattson MP. Sleep disturbances in Alzheimer’s and Parkinson’s diseases. Neuromolecular Med. 2012;14(3):194–204. https://​doi.​org/​
10.​1007/​s12017-​012-​8181-2.
4. Wang C, Bangdiwala SI, Rangarajan S, Lear SA, AlHabib KF, Mohan V, Teo K,
Poirier P, Tse LA, Liu Z, et al. Association of estimated sleep duration andnaps with mortality and cardiovascular events: a study of 116 632 people
from 21 countries. Eur Heart J. 2019;40(20):1620–9. https://​doi.​org/​10.​1093/​eurhe​artj/​ehy695.
5. Abbott RD, Ross GW, White LR, Tanner CM, Masaki KH, Nelson JS, Curb JD,
Petrovitch H. Excessive daytime sleepiness and subsequent developmentof Parkinson disease. Neurology. 2005;65(9):1442–6. https://​doi.​org/​10.​
1212/​01.​wnl.​00001​83056.​89590.​0d.
6. Leng Y, Goldman SM, Cawthon PM, Stone KL, Ancoli-Israel S, Yaffe K.
Excessive daytime sleepiness, objective napping and 11-year risk of
Parkinson’s disease in older men. Int J Epidemiol. 2018;47(5):1679–86.
https://​doi.​org/​10.​1093/​ije/​dyy098.
7. Leng Y, Stone K, Ancoli-Israel S, Covinsky K, Yaffe K. Who take naps?
Self-reported and objectively measured napping in very old women. J
Gerontol A Biol Sci Med Sci. 2018;73(3):374–9. https://​doi.​org/​10.​1093/​
gerona/​glx014.
8. Gao J, Huang X, Park Y, Hollenbeck A, Blair A, Schatzkin A, Chen H.
Daytime napping, nighttime sleeping, and Parkinson disease. Am J Epidemiol. 2011;173(9):1032–8. https://​doi.​org/​10.​1093/​aje/​kwq478.
9. Boef AGC, Dekkers OM, le Cessie S. Mendelian randomization studies: areview of the approaches used and the quality of reporting. Int J Epidemiol. 2015;44(2):496–511. https://​doi.​org/​10.​1093/​ije/​dyv071.
10. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, et al. UK biobank: an open access resource for
identifying the causes of a wide range of complex diseases of middle andold age. PLoS Med. 2015;12(3):e1001779. https://​doi.​org/​10.​1371/​journ​al.​pmed.​10017​79.

Lin et al. BMC Medicine(2024) 22:326

11. Yang MJ, Zhang Z, Wang YJ, Li JC, Guo QL, Chen X, Wang E. Associationof nap frequency with hypertension or ischemic stroke supported byprospective cohort data and Mendelian randomization in predominantly
middle-aged European subjects. Hypertension. 2022;79(9):1962–70.
https://​doi.​org/​10.​1161/​hyper​tensi​onaha.​122.​19120.
12. Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. Statistical machinelearning of sleep and physical activity phenotypes from sensor data in
96,220 UK Biobank participants. Sci Rep. 2018;8(1):7961. https://​doi.​org/​
10.​1038/​s41598-​018-​26174-1.
13. Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG. Validationof the GENEA accelerometer. Med Sci Sports Exerc. 2011;43(6):1085–93.
https://​doi.​org/​10.​1249/​MSS.​0b013​e3182​0513be.
14. White T, Westgate K, Wareham NJ, Brage S. Estimation of physical activity
energy expenditure during free-living from wrist accelerometry in UK
adults. PLoS One. 2016;11(12):e0167472. https://​doi.​org/​10.​1371/​journ​al.​pone.​01674​72.
15. Ladha C, Jackson D, Ladha K, Nappey T, Olivier P. Shaker table validation of
OpenMovement accelerometer. 2013.
16. Kelly P, Doherty AR, Mizdrak A, Marshall SJ, Kerr J, Legge A, Godbole S,
Badland HM, Oliver M, Foster C. High group level validity but high random error of a self-report travel diary, as assessed by wearable cameras. J
Transp Health. 2014;1:190–201.
17. Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P, Granat MH, WhiteT, van Hees VT, Trenell MI, Owen CG, et al. Large scale populationassessment of physical activity using wrist worn accelerometers: the UK
Biobank study. PLoS One. 2017;12(2):e0169649. https://​doi.​org/​10.​1371/​
journ​al.​pone.​01696​49.
18. Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. https://​doi.​org/​
10.​1023/A:​10109​33404​324.
19. Rabiner L, Juang B. An introduction to hidden Markov models. IEEE ASSP
Mag. 1986;3(1):4–16. https://​doi.​org/​10.​1109/​MASSP.​1986.​11653​42.
20. Carrier J, Paquet J, Morettini J, Touchette E. Phase advance of sleep andtemperature circadian rhythms in the middle years of life in humans.
Neurosci Lett. 2002;320(1–2):1–4. https://​doi.​org/​10.​1016/​s0304-​3940(02)​00038-1.
21. Li P, Gao L, Yu L, Zheng X, Ulsa MC, Yang H-W, Gaba A, Yaffe K, Bennett DA,
Buchman AS, et al. Daytime napping and Alzheimer’s dementia: a potential bidirectional relationship. Alzheimers Dementia. 2023;19(1):158–
68. https://​doi.​org/​10.​1002/​alz.​12636.
22. Cao W, Cao Z, Tian Y, Zhang L, Wang W, Tang L, Xu C, Fan D. Neutrophils
are associated with higher risk of incident amyotrophic lateral sclerosis
in a BMI- and age-dependent manner. Ann Neurol. 2023;94(5):942–54.
https://​doi.​org/​10.​1002/​ana.​26760.
23. UK Biobank: algorithmically-defined outcomes (ADOs) Version
2.0.  https://​bioba​nk.​ndph.​ox.​ac.​uk/​ukb/​ukb/​docs/​alg_​outco​me_​main.​pdf. Accessed 22 Mar 2023.
24. Zheng Z, Lv Y, Rong S, Sun T, Chen L. Physical frailty, genetic predisposition, and incident Parkinson disease. JAMA Neurol. 2023;80(5):455–61.
https://​doi.​org/​10.​1001/​jaman​eurol.​2023.​0183.
25. Thompson DJ, Wells D, Selzam S, Peneva I, Moore R, Sharp K, Tarran WA,
Beard EJ, Riveros-Mckay F, Giner-Delgado C et al. UK Biobank release andsystematic evaluation of optimised polygenic risk scores for 53 diseases
and quantitative traits. medRxiv. 2022:2022.2006.2016.22276246. https://​doi.​org/​10.​1101/​2022.​06.​16.​22276​246.
26. Dashti HS, Daghlas I, Lane JM, Huang Y, Udler MS, Wang H, Ollila HM,
Jones SE, Kim J, Wood AR, et al. Genetic determinants of daytime nappingand effects on cardiometabolic health. Nat Commun. 2021;12(1):900.
https://​doi.​org/​10.​1038/​s41467-​020-​20585-3.
27. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A,
Vukcevic D, Delaneau O, O’Connell J, et al. The UK Biobank resource withdeep phenotyping and genomic data. Nature. 2018;562(7726):203–9.
https://​doi.​org/​10.​1038/​s41586-​018-​0579-z.
28. Kim JJ, Vitale D, Otani DV, Lian MM, Heilbron K, Iwaki H, Lake J, Solsberg
CW, Leonard H, Makarious MB, et al. Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease. Nat Genet. 2024;56(1):27–36.
https://​doi.​org/​10.​1038/​s41588-​023-​01584-8.
29. Sekula P, Del Greco MF, Pattaro C, Köttgen A. Mendelian randomization
as an approach to assess causality using observational data. J Am Soc
Nephrol. 2016;27(11):3253–65. https://​doi.​org/​10.​1681/​asn.​20160​10098.

Page 11 of 12

30. Shi X, Yuan W, Cao Q, Cui W. Education plays a crucial role in the pathway
from poverty to smoking: a Mendelian randomization study. Addiction.
2023;118(1):128–39. https://​doi.​org/​10.​1111/​add.​16019.
31. Au Yeung SL, Gill D. Standardizing the reporting of Mendelian randomization studies. BMC Med. 2023;21(1):187. https://​doi.​org/​10.​1186/​
s12916-​023-​02894-8.
32. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM,
Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, et al.
Strengthening the reporting of observational studies in epidemiology
using Mendelian randomization: the STROBE-MR statement. JAMA.
2021;326(16):1614–21. https://​doi.​org/​10.​1001/​jama.​2021.​18236.
33. Gerrits MM, van Oppen P, Leone SS, van Marwijk HW, van der Horst HE,
Penninx BW. Pain, not chronic disease, is associated with the recurrenceof depressive and anxiety disorders. BMC Psychiatry. 2014;14:187. https://​doi.​org/​10.​1186/​1471-​244x-​14-​187.
34. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:
k601. https://​doi.​org/​10.​1136/​bmj.​k601.
35. Bowden J, Davey Smith G, Burgess S. Mendelian randomization withinvalid instruments: effect estimation and bias detection through Egger
regression. Int J Epidemiol. 2015;44(2):512–25. https://​doi.​org/​10.​1093/​
ije/​dyv080.
36. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using aweighted median estimator. Genet Epidemiol. 2016;40(4):304–14. https://​doi.​org/​10.​1002/​gepi.​21965.
37. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data
Mendelian randomization via the zero modal pleiotropy assumption. Int J
Epidemiol. 2017;46(6):1985–98. https://​doi.​org/​10.​1093/​ije/​dyx102.
38. Greco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in
Mendelian randomisation studies with summary data and a continuous
outcome. Stat Med. 2015;34(21):2926–40. https://​doi.​org/​10.​1002/​sim.​
6522.
39. Wu Q, Liu S, Huang X, Liu J, Wang Y, Xiang Y, Tang X, Xu Q, Yan X, Tang
B, et al. Bidirectional Mendelian randomization study of psychiatricdisorders and Parkinson’s disease. Front Aging Neurosci. 2023;15:1120615.
https://​doi.​org/​10.​3389/​fnagi.​2023.​11206​15.
40. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal
pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8.
https://​doi.​org/​10.​1038/​s41588-​018-​0099-7.
41. Bowden J, Spiller W, Del Greco MF, Sheehan N, Thompson J, Minelli C,
Davey Smith G. Improving the visualization, interpretation and analysis of
two-sample summary data Mendelian randomization via the Radial plot
and Radial regression. Int J Epidemiol. 2018;47(4):1264–78. https://​doi.​
org/​10.​1093/​ije/​dyy101.
42. Sohail S, Yu L, Schneider JA, Bennett DA, Buchman AS, Lim ASP. Sleep
fragmentation and Parkinson’s disease pathology in older adults without
Parkinson’s disease. Mov Disord. 2017;32(12):1729–37. https://​doi.​org/​10.​
1002/​mds.​27200.
43. Nassan M, Videnovic A. Circadian rhythms in neurodegenerative
disorders. Nat Rev Neurol. 2022;18(1):7–24. https://​doi.​org/​10.​1038/​
s41582-​021-​00577-7.
44. Villafuerte G, Miguel-Puga A, Rodríguez EM, Machado S, Manjarrez E,
Arias-Carrión O. Sleep deprivation and oxidative stress in animal models:a systematic review. Oxid Med Cell Longev. 2015;2015:234952. https://​doi.​org/​10.​1155/​2015/​234952.
45. Nair D, Zhang SX, Ramesh V, Hakim F, Kaushal N, Wang Y, Gozal D. Sleep
fragmentation induces cognitive deficits via nicotinamide adenine
dinucleotide phosphate oxidase-dependent pathways in mouse. Am
J Respir Crit Care Med. 2011;184(11):1305–12. https://​doi.​org/​10.​1164/​
rccm.​201107-​1173OC.
46. Jenner P. Oxidative stress in Parkinson’s disease. Ann Neurol. 2003;53
Suppl 3:S26–36. discussion S36–28. https://​doi.​org/​10.​1002/​ana.​10483.
47. Aston-Jones G, Chen S, Zhu Y, Oshinsky ML. A neural circuit for circadian
regulation of arousal. Nat Neurosci. 2001;4(7):732–8. https://​doi.​org/​10.​1038/​89522.
48. Park JS, Davis RL, Sue CM. Mitochondrial dysfunction in Parkinson’s disease: new mechanistic insights and therapeutic perspectives. Curr Neurol
Neurosci Rep. 2018;18(5):21. https://​doi.​org/​10.​1007/​s11910-​018-​0829-3.

Lin et al. BMC Medicine(2024) 22:326

49. Kudo T, Loh DH, Truong D, Wu Y, Colwell CS. Circadian dysfunction ina mouse model of Parkinson’s disease. Exp Neurol. 2011;232(1):66–75.
https://​doi.​org/​10.​1016/j.​expne​urol.​2011.​08.​003.
50. Jacobs BM, Belete D, Bestwick J, Blauwendraat C, Bandres-Ciga S, Heilbron
K, Dobson R, Nalls MA, Singleton A, Hardy J, et al. Parkinson’s disease
determinants, prediction and gene-environment interactions in the UK
Biobank. J Neurol Neurosurg Psychiatry. 2020;91(10):1046–54. https://​doi.​
org/​10.​1136/​jnnp-​2020-​323646.
51. Qu H, Wang H, Deng M, Wei H, Deng H. Associations between longer
habitual day napping and non-alcoholic fatty liver disease in an elderly
Chinese population. PLoS One. 2014;9(8):e105583. https://​doi.​org/​10.​
1371/​journ​al.​pone.​01055​83.
52. Gopinath A, Mackie PM, Phan LT, Tansey MG, Khoshbouei H. The complex
role of inflammation and gliotransmitters in Parkinson’s disease. Neurobiol Dis. 2023;176: 105940. https://​doi.​org/​10.​1016/j.​nbd.​2022.​105940.
53. Qu Y, Li J, Qin Q, Wang D, Zhao J, An K, Mao Z, Min Z, Xiong Y, Li J, et al.
A systematic review and meta-analysis of inflammatory biomarkers in
Parkinson’s disease. NPJ Parkinsons Dis. 2023;9(1):18. https://​doi.​org/​10.​1038/​s41531-​023-​00449-5.

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